Automated vehicle (AV)
The effect of automated vehicles on traffic may change as adoption grows. In this video's modeled setting, early automated vehicles keep generous following gaps and can lower network efficiency. When more vehicles become equipped and coordinated operation develops, their mutual gaps shrink and the effect can reverse.
The first act makes that change visible in traffic rather than treating automation as an immediate capacity gain. A baseline then follows adoption without financial support. The subsidy scenario asks whether encouraging earlier adoption can shorten the period of reduced efficiency and improve longer-run benefits enough to justify public spending. Dynamic indicators track adoption, network performance, and net payoff, ending with a comparison of time spent below the original baseline and cumulative value. The core insight is temporal: a technology with a difficult transition can still have a favorable mature state. A policy must be evaluated across that whole transition, not only at its first or final year.
A monthly travel-credit allowance creates a new decision beyond whether to drive today: how much should be saved for trips that may arise later? A routine commute can often be shifted to transit, while an unexpected urgent trip may be much harder to replace.
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Delivery vehicles compete for a small number of curb spaces. Without a schedule, an arriving vehicle may find every bay occupied, block the adjacent lane, and delay both other deliveries and through traffic. Reserving a time window is one way to coordinate access before a vehicle reaches the curb.
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An emission target can be difficult to reach if electric vehicles remain expensive or inconvenient for drivers. Purchase rebates can move adoption upward, but their effect may slow when charging access becomes the next obstacle.
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